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Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology


The increasing volume of whole-genome sequence (WGS) and multi-omics data requires new approaches for analysis. As one solution, we have created the cloud-based Analysis Commons, which brings together genotype and phenotype data from multiple studies in a setting that is accessible by multiple investigators. This framework addresses many of the challenges of multicenter WGS analyses, including data-sharing mechanisms, phenotype harmonization, integrated multi-omics analyses, annotation and computational flexibility. In this setting, the computational pipeline facilitates a sequence-to-discovery analysis workflow illustrated here by an analysis of plasma fibrinogen levels in 3,996 individuals from the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) WGS program. The Analysis Commons represents a novel model for translating WGS resources from a massive quantity of phenotypic and genomic data into knowledge of the determinants of health and disease risk in diverse human populations.

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Figure 1: Analysis Commons design.
Figure 2: Plasma fibrinogen association results.


  1. 1

    Psaty, B.M. et al. Circ Cardiovasc Genet 2, 73–80 (2009).

    Article  Google Scholar 

  2. 2

    Morrison, A.C. et al. Nat. Genet. 45, 899–901 (2013).

    CAS  Article  Google Scholar 

  3. 3

    Fuchsberger, C. et al. Nature 536, 41–47 (2016).

    CAS  Article  Google Scholar 

  4. 4

    Sankar, P.L. & Parker, L.S. Genet. Med. 19, 743–750 (2017).

    Article  Google Scholar 

  5. 5

    Zheng, X. et al. Bioinformatics 33, 2251–2257 (2017).

    Article  Google Scholar 

  6. 6

    Liu, X. et al. J. Med. Genet. 53, 111–112 (2016).

    CAS  Article  Google Scholar 

  7. 7

    Reid, J.G. et al. BMC Bioinformatics 15, 30 (2014).

    Article  Google Scholar 

  8. 8

    ENCODE Project Consortium Nature 489, 57–74 (2012).

  9. 9

    Lumley, T., Brody, J.A., Peloso, G.M. & Rice, K. Preprint at (2016).

  10. 10

    Wu, M.C. et al. Am. J. Hum. Genet. 89, 82–93 (2011).

    CAS  Article  Google Scholar 

  11. 11

    Huffman, J.E. et al. Blood 126, e19–e29 (2015).

    CAS  Article  Google Scholar 

  12. 12

    Kircher, M. et al. Nat. Genet. 46, 310–315 (2014).

    CAS  Article  Google Scholar 

Download references


TOPMed. WGS for the TOPMed program was supported by the NHLBI. WGS for 'NHLBI TOPMed: Whole Genome Sequencing and Related Phenotypes in the Framingham Heart Study' (phs000974.v1.p1) and 'NHLBI TOPMed: Genetics of Cardiometabolic Health in the Amish' (phs000956.v1.p1) was performed at the Broad Institute of MIT and Harvard (HHSN268201500014C and 3R01HL121007-01S1 (NHLBI, B.D.M.)). Centralized read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1). Phenotype harmonization, data management, sample-identity quality control and general study coordination were provided by the TOPMed Data Coordinating Center (3R01HL-120393-02S1 (NHLBI, B.M.P., K.M.R. and S.S.R.)). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. The infrastructure for the Analysis Commons is additionally supported by R01HL105756 (NHLBI, B.M.P.), U01HL130114 (NHLBI, B.M.P.) and 5RC2HL102419 (NHLBI, E.B.).

Old Order Amish Study. This investigation was supported by National Institutes of Health grants R01 HL121007 (NHLBI, B.D.M.), U01 GM074518, U01 HL084756 (NHLBI, J.R.O.), U01 HL137181 (NHLBI, J.R.O.) and K23 GM102678 (NIGMS, J.P.L.), as well as Mid-Atlantic Nutrition and Obesity Research Center grant P30 DK072488 (NIDDK, B.D.M.). We also gratefully acknowledge our Amish liaisons and field workers and the extraordinary cooperation and support of the Amish community.

Framingham Heart Study. The Framingham Heart Study was supported by the NHLBI Framingham Heart Study (contract no. N01-HC-25195 and HHSN268201500001I (NHLBI, R.S.V. and L.A.C.)), Fibrinogen measurement was supported by NIH R01-HL-48157. J.E.H. and A.D.J. were supported by NHLBI Intramural Research Program funds. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the NHLBI, the National Institutes of Health or the US Department of Health and Human Services.

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Correspondence to Jennifer A Brody or L Adrienne Cupples.

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Competing interests

B.M.P. reports serving on the data and safety monitoring board for a clinical trial funded by the manufacturer Zoll LifeCor and on the Steering Committee for the Yale Open Data Access Project funded by Johnson & Johnson. J.R.O. has a consulting agreement with Regeneron Pharmaceuticals that focuses on development of statistical analysis and software tools. A.C. and D.C.A. are employed by DNAnexus.

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A full list of members and affiliations appears in the Supplementary Note.

A full list of members and affiliations appears in the Supplementary Note.

A full list of members and affiliations appears in the Supplementary Note.

A full list of members and affiliations appears in the Supplementary Note.

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Brody, J., Morrison, A., Bis, J. et al. Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology. Nat Genet 49, 1560–1563 (2017).

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